In certain sensitive locations where security is a concern (e.g., airports, train stations, military bases), people and objects are often closely monitored to detect suspicious (e.g., potentially dangerous and/or malicious) activity such as the leaving of objects (e.g., unattended bags, stopped vehicles, etc.) and other activity that might indicate a security threat.
Many existing left object detection applications for monitoring such activity rely on explicit long-term tracking of multiple objects in a scene, and a left object is signaled when one or more tracked objects come to a standstill for a preset period of time. Since accurate long-term tracking of multiple objects in general scenes is still an unsolved problem, these methods are prone to error due to confusion, caused, for example, by occlusions and normal variations in ambient illumination and local changes due to shadows cast by static structures such as buildings, tress, poles, etc. These errors often result in false alarms being generated, e.g., where innocent activity or movement is mistaken for suspicious activity. Thus, a significant amount of time and resources may be wasted on relatively trivial occurrences and panic may be unnecessarily generated. Alternatively, methods that operate on a reduced sensitivity in order to compensate for this tendency to generate false alarms often tend to overlook real security threats, which can also have disastrous consequences.
Therefore, there is a need in the art for a method and apparatus for detecting left objects that is capable of detecting such objects with a low false alarm rate.